{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AOZY53TUCH5WHSVWOCUWJOIQ3C","short_pith_number":"pith:AOZY53TU","schema_version":"1.0","canonical_sha256":"03b38eee7411fb63cab670a964b910d898ddd6188d87842d4e914d551e44e846","source":{"kind":"arxiv","id":"1803.09827","version":1},"attestation_state":"computed","paper":{"title":"Vibrational properties of metastable polymorph structures by machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Ambroise van Roekeghem, Fleur Legrain, Georg K. H. Madsen, Jesus Carrete, Natalio Mingo, Stefano Curtarolo","submitted_at":"2018-03-26T20:28:02Z","abstract_excerpt":"Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on only 121 metastable structures of KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\\textrm\\"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1803.09827","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2018-03-26T20:28:02Z","cross_cats_sorted":[],"title_canon_sha256":"e04727699a3d13796098eccc0fbfcd10db57d60fc1f26f5c144c4b668813eada","abstract_canon_sha256":"f74bd910055e644383557d44bd87f42a9a80eceb6d62c90c578e2d9c6f59ea54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:10.374640Z","signature_b64":"eajpDlskiEOtzCi/uGKeItXMsGTy7olEf6dA3v6PPYUERMzxzIOBfM7Dt4hFIjnFDjxvlfPhOsmiUzL4ZWNCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03b38eee7411fb63cab670a964b910d898ddd6188d87842d4e914d551e44e846","last_reissued_at":"2026-05-18T00:20:10.373813Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:10.373813Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Vibrational properties of metastable polymorph structures by machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Ambroise van Roekeghem, Fleur Legrain, Georg K. H. Madsen, Jesus Carrete, Natalio Mingo, Stefano Curtarolo","submitted_at":"2018-03-26T20:28:02Z","abstract_excerpt":"Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on only 121 metastable structures of KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\\textrm\\"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.09827","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1803.09827","created_at":"2026-05-18T00:20:10.373940+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.09827v1","created_at":"2026-05-18T00:20:10.373940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.09827","created_at":"2026-05-18T00:20:10.373940+00:00"},{"alias_kind":"pith_short_12","alias_value":"AOZY53TUCH5W","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AOZY53TUCH5WHSVW","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AOZY53TU","created_at":"2026-05-18T12:32:13.499390+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C","json":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C.json","graph_json":"https://pith.science/api/pith-number/AOZY53TUCH5WHSVWOCUWJOIQ3C/graph.json","events_json":"https://pith.science/api/pith-number/AOZY53TUCH5WHSVWOCUWJOIQ3C/events.json","paper":"https://pith.science/paper/AOZY53TU"},"agent_actions":{"view_html":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C","download_json":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C.json","view_paper":"https://pith.science/paper/AOZY53TU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.09827&json=true","fetch_graph":"https://pith.science/api/pith-number/AOZY53TUCH5WHSVWOCUWJOIQ3C/graph.json","fetch_events":"https://pith.science/api/pith-number/AOZY53TUCH5WHSVWOCUWJOIQ3C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C/action/storage_attestation","attest_author":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C/action/author_attestation","sign_citation":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C/action/citation_signature","submit_replication":"https://pith.science/pith/AOZY53TUCH5WHSVWOCUWJOIQ3C/action/replication_record"}},"created_at":"2026-05-18T00:20:10.373940+00:00","updated_at":"2026-05-18T00:20:10.373940+00:00"}